Feasibility and Safety of a “Shared Care” Model in Complex Hepatopancreatobiliary Surgery
Bibliographic record
Abstract
OBJECTIVE: To determine the safety of a fully functioning shared care model (SCM) in hepatopancreatobiliary surgery through evaluating outcomes in pancreaticoduodenectomy. BACKGROUND: SCMs, where a team of surgeons share in care delivery and resource utilization, represent a surgeon-level opportunity to improve system efficiency and peer support, but concerns around clinical safety remain, especially in complex elective surgery. METHODS: Patients who underwent pancreaticoduodenectomy between 2016 and 2020 were included. Adoption of shared care was demonstrated by analyzing shared care measures, including the number of surgeons encountered by patients during their care cycle, the proportion of patients with different consenting versus primary operating surgeon (POS), and the proportion of patients who met their POS on the day of surgery. Outcomes, including 30-day mortality, readmission, unplanned reoperation, sepsis, and length of stay, were collected from the institution's National Surgical Quality Improvement Program (NSQIP) database and compared with peer hospitals contributing to the pancreatectomy-specific NSQIP collaborative. RESULTS: Of the 174 patients included, a median of 3 surgeons was involved throughout the patients' care cycle, 69.0% of patients had different consenting versus POS and 57.5% met their POS on the day of surgery. Major outcomes, including mortality (1.1%), sepsis (5.2%), and reoperation (7.5%), were comparable between the study group and NSQIP peer hospitals. Length of stay (10 day) was higher in place of lower readmission (13.2%) in the study group compared with peer hospitals. CONCLUSIONS: SCMs are feasible in complex elective surgery without compromising patient outcomes, and wider adoption may be encouraged.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".